Data processing system with machine learning engine to provide output generating functions

ABSTRACT

Systems, methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests to generate an output are provided. In some examples, user information may be received by a system and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. A user interface may be generated including instructions for executing the identified tests. Upon initiating a test, data may be collected from one or more sensors in the computing device. The data collected may be transmitted to the system and may be processed using one or more machine learning datasets to generate an output.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to co-pendingU.S. application Ser. No. 15/727,226, now U.S. Pat. No. 10,140,199,filed on Oct. 6, 2017, and entitled “Data Processing System with MachineLearning Engine to Provide Output Generating Functions,” which is acontinuation of and claims priority to co-pending U.S. application Ser.No. 15/716,983, filed Sep. 27, 2017, and entitled “Data ProcessingSystem with Machine Learning Engine to Provide Output GeneratingFunctions,” all of which are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

Aspects of the disclosure generally relate to one or more computersystems, servers, and/or other devices including hardware and/orsoftware. In particular, aspects are directed to executing interactivecondition evaluation tests and using machine learning to generate anoutput.

BACKGROUND

Mobile devices are being used to simplify people's lives around theworld. However, it is often difficult to collect sufficient informationvia user input. In addition, determining an accuracy of informationprovided by a user can be difficult. Often, confirming accuracy mayrequire in-person communication, additional documentation, and the like.Accordingly, executing a plurality of interactive tests generated by anentity to collect condition data, verify accuracy of data, and the like,may be advantageous.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to methods, computer-readable media,systems, and apparatuses for identifying and executing one or moreinteractive condition evaluation tests to generate an output.

In some examples, user information may be received by a system,computing device, or the like. Based on the information, one or moreinteractive condition evaluation tests may be identified. Aninstruction, command, signal or the like, may be transmitted to acomputing device of a user and executed on the computing device toenable functionality of one or more sensors that may be used in theidentified interactive condition evaluation tests.

In some examples, a user interface may be generated by the system,computing device, or the like. The user interface may includeinstructions for executing the identified interactive conditionevaluation tests. Upon initiating an interactive condition evaluationtest on the computing device of the user, data may be collected from oneor more sensors in the computing device.

In some examples, a determination may be made as to whether a triggeringevent has occurred. If not, data from the sensors may be collected. Ifso, the interactive condition evaluation test may be terminated andfunctionality associated with the sensors may be disabled.

In some arrangements, the data collected via the sensors may betransmitted to the system, computing device, or the like, and may beprocessed using one or more machine learning datasets to generate anoutput. For instance, the data may be processed to determine aneligibility of user, identify a product or service for the user, or thelike.

These and other features and advantages of the disclosure will beapparent from the additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIGS. 1A and 1B illustrate an illustrative computing environment forimplementing interactive condition evaluation test and output generatingfunctions, according to one or more aspects described herein.

FIG. 2 illustrates an example interactive test generation and controlcomputing system, according to one or more aspects described herein.

FIGS. 3A-3G depict an illustrative event sequence for performinginteractive condition evaluation test and output generating functions,according to one or more aspects described herein.

FIG. 4 illustrates one example flow chart illustrating an example methodof executing one or more interactive condition evaluation tests andgenerating an output, according to one or more aspects described herein.

FIG. 5 illustrates one example flow chart illustrating example outputgenerating functions, according to one or more aspects described herein.

FIG. 6 illustrates one example flow chart illustrating additionalinteractive condition evaluation test and output generating functions,according to one or more aspects described herein.

FIG. 7 illustrates one example user interface for executing aninteractive condition evaluation test, according to one or more aspectsdescribed herein.

FIG. 8 illustrates one example user interface for displaying a generatedoutput, according to one or more aspects described herein.

FIG. 9 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

Mobile devices are being used to perform functions that, at one time,required interaction between users, such as a customer and a vendor,service provider, or the like. However, accuracy of informationprovided, identity of a user providing input via the mobile device, andthe like, may be difficult to confirm. Accordingly, it may beadvantageous to identify and execute one or more interactive conditionevaluation tests on the mobile device to evaluate a condition of a user,determine eligibility for one or more products or services, and thelike.

In some examples, a user may request a product or service (e.g., via amobile device). The request may be transmitted to a system, computingplatform, or the like, which may process the request and transmit arequest for additional information. The user may provide the requestedadditional information via the mobile device. The additional informationmay include information such as name, age, gender, height, weight,location, and the like.

In some arrangements, based on the information provided, one or moreproducts or services for which the user may be eligible may beidentified. Based on the identified one or more products, one or moreinteractive condition evaluation tests may be identified to determineeligibility of the user.

In some examples, the system may transmit an instruction to the mobiledevice to enable one or more sensors associated with the identified oneor more interactive condition evaluation tests. The one or more testsmay then be executed by the mobile device. Data from the one or moresensors may be collected during execution of the test and may betransmitted to the system for processing. In some arrangements, thesystem may use machine learning to evaluate eligibility of the user(e.g., based on the sensor data and/or other internal and/or externaldata), generate an output for a user (e.g., a product or service tooffer), and the like.

These and other aspects will be described more fully herein.

FIGS. 1A-1B depict an illustrative computing environment forimplementing and using an interactive test generation and control systemin accordance with one or more aspects described herein. Referring toFIG. 1A, computing environment 100 may include one or more computingdevices and/or other computing systems. For example, computingenvironment 100 may include an interactive test generation and controlcomputing platform 110, an internal data computing device 120, a firstlocal user computing device 150, a second local user computing device155, an external data computing device 140, a remote user mobilecomputing device 170, and a remote user computing device 175.

Interactive test generation and control computing platform 110 may beconfigured to host and/or execute one or more modules includinginstructions for providing various interactive condition evaluation testfunctions and/or factor prediction functions. In some examples,interactive test generation and control computing platform 110 may beconfigured to receive data from a plurality of disparate sources,aggregated data, using a machine learning engine, generate one or morepredictions, generate and initiate one or more interactive conditionevaluation tests, and the like.

One or more aspects described herein may be performed by one or moreapplications downloaded or otherwise provided to a computing device(such as first local user computing device 150, second local usercomputing device 155, remote user mobile computing device 170, remoteuser computing device 175, or the like) and executing thereon. In someexamples, the one or more applications (or portions thereof) may executein a background of the device.

Although various devices in the interactive test generation and controlprocessing system are shown and described as separate devices, one ormore of interactive test generation and control computing platform 110,internal data computing device 120, external data computing device 140,first local user computing device 150, second local user computingdevice 155, remote user mobile computing device 170, and/or remote usercomputing device 175, may be part of a single computing device withoutdeparting from the invention.

Internal data computing device 120 may have, store and/or include dataobtained by an entity implementing the interactive test generation andcontrol computing platform 110 and/or stored by the entity. In someexamples, internal data computing device 120 may include data associatedwith customers, one or more insurance claims, accident histories andassociated damages, costs, etc., user information, and the like. In someexamples, internal data computing device 120 may include multiplecomputing devices storing various different types of data. In otherexamples, internal data computing device 120 may store the various typesof data. In still other examples, internal data computing device 120 mayquery databases in one or more other computing devices, systems, or thelike, to obtain data that may be used in one or more processes describedherein.

External data computing device 140 may have, store and/or include datafrom outside of or external to the entity. For instance, external datacomputing device 140 may store or provide access to publicly availableinformation, such as weather, traffic, population, demographicinformation, and the like. Additionally or alternatively, external datacomputing device 140 may store or provide access to data related tospending habits of one or more users (e.g., types of purchases made,amounts, locations of purchases, and the like). In still other examples,external data computing device 140 may store or provide access to datarelated to behaviors of users, such as frequency of gym visits, datacollected by a wearable fitness device, and the like. Various othertypes of information may be accessed via the external data computingdevice 140 without departing from the invention. In some examples,external data computing device 140 may access information from varioussources, such as via public network 195.

Local user computing device 150, 155, internal data computing system120, external data computing system 140, remote user mobile computingdevice 170, and remote user computing device 175 may be configured tocommunicate with and/or connect to one or more computing devices orsystems shown in FIG. 1A. For instance, local user computing device 150,155 and/or internal data computing device 120 may communicate with oneor more computing systems or devices via network 190, while remote usermobile computing device 170, remote user computing device 175, and/orexternal data computing device 140 may communicate with one or morecomputing systems or devices via network 195. The local and remote usercomputing devices may be used to configure one or more aspects ofinteractive test generation and control computing platform 110, displayone or more notifications, execute one or more interactive conditionevaluation tests, capture data associated with one or more interactivecondition evaluation tests, display outputs, and the like.

In one or more arrangements, internal data computing device 120, localuser computing device 150, local user computing device 155, externaldata computing device 140, remote user mobile computing device 170,and/or remote user computing device 175 may be any type of computingdevice or combination of devices capable of performing the particularfunctions described herein. For example, internal data computing device120, local user computing device 150, local user computing device 155,external data computing device 140, remote user mobile computing device170, and/or remote user computing device 175 may, in some instances, beand/or include server computers, desktop computers, laptop computers,tablet computers, smart phones, or the like that may include one or moreprocessors, memories, communication interfaces, storage devices, and/orother components. As noted above, and as illustrated in greater detailbelow, any and/or all of interactive test generation and controlcomputing platform 110, internal data computing device 120, local usercomputing device 150, local user computing device 155, external datacomputing device 140, remote user mobile computing device 170, and/orremote user computing device 175 may, in some instances, be or includespecial-purpose computing devices configured to perform specificfunctions.

Computing environment 100 also may include one or more computingplatforms. For example, and as noted above, computing environment 100may include interactive test generation and control computer platform110. As illustrated in greater detail below, interactive test generationand control computer platform 110 may include one or more computingdevices configured to perform one or more of the functions describedherein. For example, interactive test generation and control computerplatform 110 may have or include one or more computers (e.g., laptopcomputers, desktop computers, tablet computers, servers, server blades,or the like).

As mentioned above, computing environment 100 also may include one ormore networks, which may interconnect one or more of interactive testgeneration and control computer platform 110, internal data computingdevice 120, local user computing device 150, local user computing device155, external data computing device 140, remote user mobile computingdevice 170, and/or remote user computing device 175. For example,computing environment 100 may include private network 190 and publicnetwork 195. Private network 190 and/or public network 195 may includeone or more sub-networks (e.g., Local Area Networks (LANs), Wide AreaNetworks (WANs), or the like). Private network 190 may be associatedwith a particular organization (e.g., a corporation, financialinstitution, educational institution, governmental institution, or thelike) and may interconnect one or more computing devices associated withthe organization. For example, interactive test generation and controlcomputer platform 110, internal data computing device 120, local usercomputing device 150, and/or local user computing device 155, may beassociated with an organization (e.g., a financial institution), andprivate network 190 may be associated with and/or operated by theorganization, and may include one or more networks (e.g., LANs, WANs,virtual private networks (VPNs), or the like) that interconnectinteractive test generation and control computer platform 110, internaldata computing device 120, local user computing device 150, and/or localuser computing device 155, and one or more other computing devicesand/or computer systems that are used by, operated by, and/or otherwiseassociated with the organization. Public network 195 may connect privatenetwork 190 and/or one or more computing devices connected thereto(e.g., interactive test generation and control computer platform 110,internal data computing device 120, local user computing device 150,local user computing device 155) with one or more networks and/orcomputing devices that are not associated with the organization. Forexample, external data computing device 140, remote user mobilecomputing device 170, and/or remote user computing device 175 might notbe associated with an organization that operates private network 190(e.g., because external data computing device 140, remote user mobilecomputing device 170 and remote user computing device 175 may be owned,operated, and/or serviced by one or more entities different from theorganization that operates private network 190, such as one or morecustomers of the organization, public or government entities, and/orvendors of the organization, rather than being owned and/or operated bythe organization itself or an employee or affiliate of theorganization), and public network 195 may include one or more networks(e.g., the internet) that connect external data computing device 140,remote user mobile computing device 170 and remote user computing device175 to private network 190 and/or one or more computing devicesconnected thereto (e.g., interactive test generation and controlcomputer platform 110, internal data computing device 120, local usercomputing device 150, and/or local user computing device 155).

Referring to FIG. 1B, interactive test generation and control computingplatform 110 may include one or more processors 111, memory 112, andcommunication interface 113. A data bus may interconnect processor(s)111, memory 112, and communication interface 113. Communicationinterface 113 may be a network interface configured to supportcommunication between interactive test generation and control computingplatform 110 and one or more networks (e.g., private network 190, publicnetwork 195, or the like). Memory 112 may include one or more programmodules having instructions that when executed by processor(s) 111 causeinteractive test generation and control computing platform 110 toperform one or more functions described herein and/or one or moredatabases that may store and/or otherwise maintain information which maybe used by such program modules and/or processor(s) 111. In someinstances, the one or more program modules and/or databases may bestored by and/or maintained in different memory units of interactivetest generation and control computing platform 110 and/or by differentcomputing devices that may form and/or otherwise make up interactivetest generation and control computing platform 110.

For example, memory 112 may have, store, and/or include a productidentification module 112 a. The product identification module 112 a maystore instructions and/or data that may cause or enable the interactivetest generation and control computing platform 110 to receive data from,for examples, local user computing device 150, local user computingdevice 155, remote user mobile computing device 170, and/or remote usercomputing device 175 that may include a request for a product orservice, information about a user requesting the product or service orfor whom the product or service is being requested, and the like. Insome examples, the requested product may be a life or other insuranceproduct. In some arrangements, information received may include nameand/or other identifier of a user, age, gender, height, weight, and thelike. The information may be transmitted from the local user computingdevice 150, 155, remote user mobile computing device 170, remote usercomputing device 175, or the like, to the interactive test generationand control computing platform 110 and may be processed by the productidentification module 112 a to identify one or more products (e.g., alife insurance policy) to offer or recommend to the user. In someexamples, interactive tests used to determine eligibility for the one ormore products may be identified based on the identified one or moreproducts, as will be discussed more fully herein.

Memory 112 may further have, store and/or include an interactive testidentification module 112 b. The interactive test identification module112 b may store instructions and/or data that may cause or enable theinteractive test generation and control computing platform 110 togenerate or identify one or more interactive condition evaluation testsbased on one or more products identified by the product identificationmodule 112 a. For instance, one or more interactive condition evaluationtests may be identified for execution by a user. The results of theidentified one or more interactive condition evaluation tests may thenbe used, either alone or in conjunction with other data, to determinewhether a user is eligible for the one or more products, a costassociated with the products, a deductible associated with the products,a discount or refund that may be available to the user if the useraccepts the product, and the like. Some example tests may includemobility tests, cognitive skills tests, breathing or other lung capacitytests, and the like. In some examples, the tests may be executed by theuser on a mobile device, such as remote user mobile computing device170, or remote user computing device 175. Types of tests, execution oftests, and the like, will be discussed more fully herein.

Memory 112 may further have, store and/or include a sensor activationmodule 112 c. Sensor activation module 112 c may store instructionsand/or data that may cause or enable the interactive test generation andcontrol computing platform 110 to activate or enable one or more sensorsof a plurality of sensors in a user computing device, such as remoteuser mobile computing device 170, remote user computing device 175, orthe like. Some example sensors may include accelerometers, globalpositioning system (GPS) sensors, gyroscopes, pressure sensors, humiditysensors, pedometer, heart rate sensors, pulse sensors, breathingsensors, one or more cameras or other image capturing devices, and thelike. Sensors may also include components of the computing device, suchas a usage monitor, or the like) that may record or detect operation ofthe device, applications executed, contact with a display of the device,user input, and the like. Upon identifying one or more interactivecondition evaluation tests to be executed, the sensor activation module112 c may transmit a signal, instruction or command to the computingdevice (e.g., remote user mobile computing device 170, remote usercomputing device 175, or the like) activating and/or enabling one ormore sensors. In some examples, the sensors activated or enabled may besensors identified for use with the identified one or more interactivecondition evaluation tests. In some arrangements, the sensors activatedor enabled may be fewer than all sensors associated with the computingdevice.

Memory 112 may further have, store, and/or include an interfacegeneration module 112 d. The interface generation module 112 d may storeinstructions and/or data that may cause or enable the interactive testgeneration and control computing platform 110 to generate one or moreuser interfaces associated with each identified interactive conditionevaluation test. For example, for each test identified for execution,the interface generation module 112 d may generate one or more userinterfaces including, for example, information associated with eachtest, instructions for initiating and/or performing each test, and thelike. The interface generation module 112 d may transmit the userinterfaces to a user computing device, such as remote user mobilecomputing device 170, remote user computing device 175, or the like, andmay cause the user interface(s) to display on the device.

Memory 112 may further have, store and/or include a sensor data analysismodule 112 e. Sensor data analysis module 112 e may store instructionsand/or data that may cause or enable the interactive test generation andcontrol computing platform 110 to receive sensor data from a computingdevice executing one or more interactive condition evaluation tests(e.g., remote user mobile computing device 170, remote user computingdevice 175, or the like) and analyze the sensor data. In some examples,the sensor data analysis module 112 e may receive raw sensor data andmay process the data (e.g., filter, smooth, or the like) to identifydata for analysis (e.g., data to provide the most accurate analysisavailable). In some examples, one or more machine learning datasets maybe used to evaluate data from the sensor data analysis module 112 e toevaluate a condition of the user executing the test associated with thesensor data, as will be discussed more fully herein. In some examples,sensor data may include an outcome of a mobility test (e.g., walk apredetermined distance, walk a predetermined time on a treadmill at adesignated speed, or the like), an outcome of a reflex analysis (e.g.,how quickly a user responds to a prompt on the device), an outcome ofone or more cognitive skills tests (e.g., questions directed toevaluating memory, recognition, and the like), an outcome of a lungcapacity test (e.g., as determined from a force on which a user exhalesonto the computing device from a predetermined distance), and the like.

Memory 112 may further have, store and/or include a data aggregationmodule 112 f. Data aggregation module 112 f may store instructionsand/or data that may cause or enable the interactive test generation andcontrol computing platform 110 to receive data from a plurality ofsources. For instance, data may be received from one or more internalsources (e.g., internal data computing device 120) and/or from one ormore external sources (e.g., external data computing device 140). Thedata may include data associated with users (e.g., names, addresses,ages, genders, and the like), demographic information, localityinformation, behavioral information (e.g., exercise habits, each habits,etc.), purchase habits or history, medical information, and the like.Some or all of the data may be collected with permission of the user. Insome examples, one or more machine learning datasets may be used toevaluate the aggregated data, either alone or in conjunction with otherdata (e.g., sensor data, data from one or more interactive conditionevaluation tests, or the like) to determine one or more outputs, as willbe discussed more fully herein.

Interactive test generation and control computing platform 110 mayfurther have, store, and/or include a machine learning engine 112 g andmachine learning datasets 112 h. Machine learning engine 112 g andmachine learning datasets 112 h may store instructions and/or data thatcause or enable interactive test generation and control computingplatform 110 to evaluate data, such as sensor data or other data from acomputing device executing one or more interactive condition evaluationtests, aggregated data from internal sources, external sources, and thelike, to generate or determine one or more outputs (e.g., by outputgeneration module 112 i). The machine learning datasets 112 h may begenerated based on analyzed data (e.g., data from previously executedinteractive condition evaluation tests, historical data from internaland/or external sources, and the like), raw data, and/or received fromone or more outside sources.

The machine learning engine 112 g may receive data (e.g., data collectedduring one or more interactive condition evaluate tests executed by andreceived from, for example, remote user mobile computing device 170,remote user computing device 175, or the like, internal data computingdevice 120, external data computing device 140, and the like) and, usingone or more machine learning algorithms, may generate one or moremachine learning datasets 112 h. Various machine learning algorithms maybe used without departing from the invention, such as supervisedlearning algorithms, unsupervised learning algorithms, regressionalgorithms (e.g., linear regression, logistic regression, and the like),instance based algorithms (e.g., learning vector quantization, locallyweighted learning, and the like), regularization algorithms (e.g., ridgeregression, least-angle regression, and the like), decision treealgorithms, Bayesian algorithms, clustering algorithms, artificialneural network algorithms, and the like. Additional or alternativemachine learning algorithms may be used without departing from theinvention. In some examples, the machine learning engine 112 g mayanalyze data to identify patterns of activity, sequences of activity,and the like, to generate one or more machine learning datasets 112 h.

The machine learning datasets 112 h may include machine learning datalinking one or more outcomes of an interactive condition evaluationtest, types or amounts of sensor data, historical behavioral data,transaction data, health data, or the like (or combinations thereof) toone or more outputs. For instance, data may be used to generate one ormore machine learning datasets 112 h linking data from interactivecondition evaluation tests, internal user data, external user data, andthe like, to outputs, such as a mortality rate, likelihood of developingone or more illnesses or diseases, and the like. This information may beused to evaluate a risk associated with a user requesting a product orservice (e.g., a life insurance product or service) to determine apremium of an insurance policy, a discount, rebate or other incentive tooffer to the user, and the like. In some examples, the information maybe used to evaluate risk associated with a user requesting an auto orhome product or service (e.g., insurance product). The information maybe used to determine a premium, deductible, incentive, or the like.

The machine learning datasets 112 h may be updated and/or validatedbased on later-received data. For instance, as additional interactivecondition evaluation tests are executed, data is collected or receivedfrom internal data computing device 120, external data computing device140, and the like, the machine learning datasets 112 h may be validatedand/or updated based on the newly received information. Accordingly, thesystem may continuously refine determinations, outputs, and the like.

The machine learning datasets 112 h may be used by, for example, anoutput generation module 112 i stored or included in memory 112. Theoutput generation module 112 i may store instructions and/or dataconfigured to cause or enable the interactive test generation andcontrol computing platform 110 to generate one or more outputs based onthe machine learning dataset 112 h analysis of data (e.g., sensor data,aggregate data, and the like). For instance, as discussed above, theoutput generation module 112 i may generate one or more premiums,discounts, incentives, or the like, related to a product identified fora user, requested by a user, or the like. In some examples, the outputgeneration module 112 i may transmit the generated output to a computingdevice, such as remote user mobile computing device 170, remote usercomputing device 175, or the like, and may cause the generated output todisplay on the device. In some arrangements, the output may betransmitted to the computing device from which the user requested aproduct, on which the one or more interactive condition evaluation testswere executed, or the like.

FIG. 2 is a diagram of an illustrative interactive test generation andcontrol system 200 including an interactive test generation and controlserver 210, an external computing device 240, a mobile device 250, andadditional related components. Each component shown in FIG. 2 may beimplemented in hardware, software, or a combination of the two.Additionally, each component of the interactive test generation andcontrol system 200 may include a computing device (or system) havingsome or all of the structural components described herein for computingdevice 901 in FIG. 9. The interactive test generation and control system200 may also include or be in communication with one or more computingplatforms, servers, devices, and the like, shown and described withrespect to FIGS. 1A and 1B.

One or more components shown in FIG. 2, interactive test generation andcontrol server 210, external data computing device 240, and/or mobiledevice 250 may communicate with each other via wireless networks orwired connections, and each may communicate additional mobile computingdevices, other remote user computing devices (e.g., remote usercomputing device 170) and/or a number of external computer servers,devices, etc. 210, 240, over one or more communication networks 230. Insome examples, the mobile computing device 250 may be paired (e.g., viaBluetooth™ technology) to one or more other devices (e.g., another userpersonal mobile computing device, such as a wearable device, tablet,etc.). If the device is no longer in proximity to be paired (e.g.,mobile computing device 250 is no longer near enough to another userpersonal mobile computing device to be paired) a notification may begenerated and displayed on the device 250 (e.g., to indicate that youmay have left a device behind).

As discussed herein, the components of interactive test generation andcontrol system 200, operating individually or using communication andcollaborative interaction, may perform such features and functions suchas identifying one or more products or services, identifying one or moreinteractive condition evaluation tests, executing one or moreinteractive condition evaluation tests, collecting data associated withone or more interactive condition evaluation tests, retrieving data fromone or more internal and/or external sources, generating an output, andthe like.

Interactive test generation and control system 200 may include one ormore mobile devices 250. Mobile device 250 may be, for example,smartphones or other mobile phones, personal digital assistants (PDAs),tablet computers, laptop computers, wearable devices such as smartwatches and fitness monitors, and the like. Mobile device 250 mayinclude some or all of the elements described herein with respect to thecomputing device 901.

Mobile device 250 may include a network interface 251, which may includevarious network interface hardware (e.g., adapters, modems, wirelesstransceivers, etc.) and software components to enable mobile device 250to communicate with interactive test generation and control server 210,external computing device 240, and various other external computingdevices. One or more specialized software applications, such as testanalysis application 252 may be stored in the memory of the mobiledevice 250. The test analysis application(s) 252 may be received vianetwork interface 251 from the interactive test generation and controlserver 210, or other application providers (e.g., public or privateapplication stores). Certain test analysis applications 252 might notinclude user interface screens while other applications 252 may includeuser interface screens that support user interaction. Such applications252 may be configured to run as user-initiated applications or asbackground applications. The memory of mobile device 250 also mayinclude databases configured to receive and store sensor data receivedfrom mobile device sensors, usage type, application usage data, and thelike. Although aspects of the test analysis software application(s) 252are described as executing on mobile device 250, in various otherimplementations, some or all of the test analysis functionalitydescribed herein may be implemented by interactive test generation andcontrol server 210.

As discussed herein, mobile device 250 may include various componentsconfigured to generate and/or receive data associated with execution ofone or more interactive condition evaluation tests by or on the mobiledevice 250, and/or data associated with usage of the mobile device 250.For example, using data from sensors 253 (e.g., 1-axis, 2-axis, or3-axis accelerometers, compasses, speedometers, vibration sensors,pressure sensors, gyroscopic sensors, etc.) and/or GPS receivers orother location-based services (LBS) 254, an application 252 (or otherdevice or module, e.g., interactive test generation and control server210) may determine movement of the mobile device 250, evaluate actionsperformed with or on the mobile device 250, and the like. The sensors253 and/or GPS receiver or LBS component 254 of a mobile device 250 mayalso be used to determine speeds (e.g., walking pace, running pace,etc.), force on mobile device, response times for providing input to themobile device, and the like.

Mobile device 250 may further include a usage monitor 255. The usagemonitor may be a device (e.g., including a processor, etc.) and mayinclude hardware and/or software configured to monitor various aspectsof the usage of the mobile device 250. For instance, the usage monitor255 may monitor a number of minutes, hours, or the like the device is inuse (e.g., based on factors such as device being illuminated, userinteracting with or looking at the device, etc.). Further, the usagemonitor 255 may monitor which applications are used above a thresholdamount of time in a predetermined time period (e.g., one day, one week,one month, or the like). In still other examples, the usage monitor 255may determine a type of motion or speed of motion associated withmovement of the mobile device 250, whether the device is maintainedwithin a case, and the like. Additional aspects of device usage may bemonitored without departing from the invention. Data related to usage ofthe mobile device 250 may be used to determine one or more outputs(e.g., may indicate decreased mobility, inactive lifestyle, and thelike).

The mobile device 250 may be configured to establish communicationinteractive test generation and control server 210 via one or morewireless networks (e.g., network 230).

The system 200 may further include an external data computing device240. External data computing device 240 may store or receive data fromone or more external data sources, such as user information, healthinformation, automotive information (e.g., driving behaviors,operational parameters, make, model, trim, etc.), transactioninformation, user behavioral information, and the like. This informationmay be aggregated and process, for instance, by interactive testgeneration and control server 240, to generate one or more outputs. Theexternal data computing device 240 may include an external data databasethat may store data from one or more external sources for use ingenerating one or more outputs.

The system 200 also may include one or more external servers, such asinteractive test generation and control server 210 which may containsome or all of the hardware/software components as the computing device901 depicted in FIG. 9.

The interactive test generation and control server 210 may include someor all of the components and/or functionality described with respect toFIGS. 1A and 1B. The server 210 may include one or more databases 212configured to store data associated with, for example, data internal tothe entity (e.g., user or customer data, historical data relating toclaims, accidents, and the like), that may be used to evaluate risk.Further, the server 210 may include test performance analysis module 211which may provide some or all of the operations and/or functionalitydescribed with respect to FIGS. 1A and 1B.

FIGS. 3A-3G illustrate one example event sequence for executing one ormore interactive condition evaluation tests and determining an output inaccordance with one or more aspects described herein. The sequenceillustrated in FIGS. 3A-3G is merely one example sequence and variousother events may be included, or events shown may be omitted, withoutdeparting from the invention.

With reference to FIG. 3A, in step 301, a request for a particularproduct or service, or type of product or service may be received by auser computing device, such as remote user mobile computing device 170.The request may include a request to purchase the particular product orservice. In some examples, the request may include informationassociated with a user for whom the request is made (e.g., name, contactinformation, and the like).

In step 302, the request may be transmitted from the remote user mobilecomputing device 170 to the interactive test generation and controlcomputing platform 110. The request may be received by the interactivetest generation and control computing platform 110 in step 303 and mayprocess the request.

In step 304, a request for additional user information may be generated.The request may include a request for information associated with theparticular user, such as age, gender, location, occupation, tobaccousage, and the like. In step 305, the request for additional userinformation may be transmitted to the remote user mobile computingdevice 170 and, in step 306, the request for additional information maybe received by the remote user mobile computing device 170.

With reference to FIG. 3B, in step 307, the requested additional userinformation may be received by the remote user mobile computing device170. In step 308, the received additional information may be transmittedto the interactive test generation and control computing platform 110.

In step 309, the received additional information may be processed toidentify one or more products or services to offer to the user that meetthe request provided by the user (e.g., if the user has requested a lifeinsurance policy, the computing platform 110 may identify one or morelife insurance policies that may be suitable for the user based on theuser information and that may be offered to the user).

In step 310, a request for data may be generated. For instance, theinteractive test generation and control computing platform 110 maygenerate one or more requests for data associated with the user. Therequests may include data related to health information of the user,spending habits or other transaction information, lifestyle information,driving behaviors, insurance claim information, and the like. The datarequests may be transmitted to an external data computing device 140 instep 311 and/or an internal data computing device 120 in step 312. Insome examples, requests for data may be transmitted to additionalcomputing devices. In some arrangements, the requests for data mayinclude a name or other unique identifier of a user that may be used asinput in a query to identify the desired data.

With reference to FIG. 3C, the request for data may be received by theexternal data computing device 140 in step 313 and the internal datacomputing device 120 in step 314. In steps 315 and 316, the requesteddata may be extracted from the external data computing device 140 andinternal data computing device 120, respectively. In step 317, dataextracted from the external data computing device 140 may be transmittedto the interactive test generation and control computing platform 110.In step 318, data extracted from the internal data computing device 120may be transmitted to the interactive test generation and controlcomputing platform 110.

With reference to FIG. 3D, in step 319, the extracted data may bereceived and, in step 320, the extracted data may be aggregated. In someexamples, step 320 of aggregating the data may be optional.

In step 321, one or more interactive condition evaluation tests todetermine eligibility for the one or more identified products may beidentified. For instance, based on the one or more products or servicesidentified for the user, one or more interactive condition evaluationtests may be identified. In some examples, a plurality of differenttypes of interactive condition evaluation tests may be stored and, instep 321, one or more of the plurality of tests may be selected oridentified for execution on the remote user mobile computing device 170.Particular types of tests will be discussed more fully herein.

For instance, data associated with the user may be used to identify oneor more products to offer to the user and the identified one or moreproducts may be used to identify one or more interactive conditionevaluation tests to execute. In some examples, user information (e.g.,age, health information, and the like) may also be used in identifyingone or more interactive condition evaluation tests to and/or indetermining parameters of one or more interactive condition evaluationtests. For instance, if the system identifies a first test as a timedtreadmill test in which a user must walk on a treadmill for apredetermined distance (as measured by the remote user mobile computingdevice 170), the required distance may be modified based on an age of auser and/or an expected time (or time to fit into a particular category)may be modified based on the age of the user. Accordingly, in oneexample, a 65 year old user requesting life insurance may be given atest having a shorter distance or a long expected time than a 25 yearold user requesting life insurance.

In step 322, one or more interactive condition evaluation test functionsmay be initiated by the interactive test generation and controlcomputing platform 110. For instance, upon identifying one or more testsfor execution, one or more functions associated with administering thetests (e.g., generating interfaces including instructions, transmittinginterfaces, processing received data, and the like) may be enabled oractivated by or within the interactive test generation and controlcomputing platform 110. In some examples, upon completion of the testingprocess (e.g., upon generating an output) the enabled or activatedfunctions may be disabled or deactivated in order to conserve computingresources.

In step 323, an instruction to activate one or more sensors in theremote user mobile computing device 170 may be generated and transmittedto the remote user mobile computing device 170. For instance, uponidentifying one or more interactive condition evaluation tests forexecution by the remote user mobile computing device 170, theinteractive test generation and control computing platform 110 mayidentify one or more sensors within the remote user mobile computingdevice 170 that may be used to collect data associated with theidentified tests and may transmit an instruction to the remote usermobile computing device 170 to activate or enable the identifiedsensors. In step 324, the instruction may be received by the remote usermobile computing device 170 and may be executed to activate theidentified sensors.

With reference to FIG. 3E, in step 325, a user interface associated witha first test of the identified one or more interactive conditionevaluation tests may be generated. In some examples, the user interfacemay include instructions for executing the first test. In step 326, thegenerated user interface may be transmitted to the remote user mobilecomputing device 170 and, in step 327, the user interface may bedisplayed on a display of the remote user mobile computing device 170.

In step 328, the first test may be initiated and sensor data associatedwith the first test may be collected. For instance, data from one ormore sensors monitoring movement, speed, position, and the like, of theremote user mobile computing device 170 may be collected. In someexamples, data may be collected based on interaction with one or moreuser interfaces (e.g., response times, etc.). In step 329, the sensordata may be transmitted from the remote user mobile computing device 170to the interactive test generation and control computing platform 110.

With reference to FIG. 3F, the sensor data may be received in step 330.In step 331, if additional tests have been identified for execution, auser interface for a second interactive condition evaluation test may begenerated. The user interface may include instructions and/or parametersfor executing the second interactive condition evaluation test by orwith the remote user mobile computing device 170.

In step 332, the user interface may be transmitted to the remote usermobile computing device 170 and, in step 333, the user interface may bedisplayed on a display of the remote user mobile computing device 170.

In step 334, sensor data associated with execution of the secondinteractive condition evaluation test may be collected and, in step 335,the collected sensor data may be transmitted to the interactive testgeneration and control computing platform 110.

With reference to FIG. 3G, in step 336, sensor data associated with thesecond interactive condition evaluation test may be received. In step337, the received sensor data (e.g., from the first test, second test,and any other tests) and/or other data (e.g., data from internal sources120, data from external sources 140, and the like) may be analyzed. Insome examples, analyzing the data may include comparing the data to oneor more machine learning datasets.

In step 338, an output may be generated based on the analysis of thesensor data and/or other data. For instance, based on the comparison ofthe data to the one or more machine learning datasets, an output may begenerated. In some examples, the generated output may be a lifeinsurance policy having parameters generated based on the analysis ofthe data. Additionally or alternatively, a premium associated with thelife insurance policy may also be generated as an output. In still otherexamples, a discount, rebate or other incentive may be generated as anoutput. For instance, if tobacco use is detected, the system maygenerate an incentive such as a rebate if the user stops tobacco use andsubmits to a subsequent interactive condition evaluation test to confirmthe tobacco use has stopped.

Various other outputs may be generated without departing from theinvention.

In step 339, the generated output may be transmitted to, for instance,the remote user mobile computing device 170. Additionally oralternatively, the generated output may be transmitted to anothercomputing device, such as local computing device 150, local computingdevice 155, and/or remote user computing device 175.

In step 340, the generated output may be displayed on the remote usermobile computing device 170. In some examples, displaying the generatedoutput may include an option to accept the offered product or service,identified parameters, and the like. Selection of this option may bindthe user and product or service provider. Accordingly, by executing theinteractive condition evaluation tests and providing results to theinteractive test generation and control computing platform, the user mayobtain the desired product or service without submitting to a formalunderwriting process, which may include a physical examination, and thelike.

FIG. 4 illustrates one example process for generating and evaluatinginteractive condition evaluations tests and/or other data, to generatean output according to one or more aspects described herein. The stepsdescribed with respect to FIG. 4 may be performed by one or more of thevarious devices described herein, such as the interactive testgeneration and control computing platform 110, the interactive testgeneration and control server 210, remote user mobile computing device,and the like. In some examples, one or more of the processes or stepsdescribed may be performed in real-time or near real-time.

In step 400, a request for a product may be received. In some examples,the request may be received from a user computing device, such as remoteuser mobile computing device 170. In step 402, user information may bereceived from, for instance, the remote user mobile computing device170. In some examples, the user information may include informationrequested by, for instance, the interactive test generation and controlcomputing platform 110 and may include information such as age, gender,location, and the like.

In step 404, one or more products and interactive tests may beidentified. For instance, the received user information may be used toidentify one or more products for which the user may be eligible andthat meet the request for the product. Based on the identified one ormore products, one or more interactive condition evaluation tests may beidentified to determine whether the user is eligible for the identifiedone or more products.

In step 406, a user interface including instructions for executing aninteractive condition evaluation test of the identified one or moreinteractive condition evaluation tests may be generated and transmittedto, for instance, the remote user mobile computing device 170. In step408, an instruction or command may be transmitted to, for instance, theremote user mobile computing device 170 to activate one or more sensorsassociated with the interactive condition evaluation test and initiatethe interactive condition evaluation test.

In step 410, data may be collected from one or more sensors, monitoringor usage devices, or the like, associated with the remote user mobilecomputing device 170. For instance, data from sensors associated withthe interactive condition evaluation test being executed may becollected and/or transmitted to the interactive test generation andcontrol computing platform 110.

In step 412, a determination is made as to whether a triggering eventhas occurred. In some examples, a triggering event may include anindication that a test is complete, that one or more parameters orcriteria of the test have been met, that a threshold amount of data hasbeen received, or the like. If, in step 412, a triggering event has notoccurred, the process may return to step 410 to continue collectingdata.

If, in step 412, a triggering event has occurred, the interactivecondition evaluation test may be terminated (e.g., the interactive testgeneration and control computing platform 110 may transmit aninstruction, signal or command to terminate the test and, in someexamples, disable or deactivate one or more sensors activated forexecution of the interactive condition evaluation test.

In step 416, a determination may be made as to whether there areadditional tests identified for execution (e.g., a second or more testidentified in step 404). If so, the process may return to step 406 andmay generate and transmit instructions for a second test, etc.

If, in step 416, a determination is made that there are no additionaltests identified for execution, the collected data may be processed instep 418. In some examples, the collected data may be processed itself.In other examples, the collected data may be processed with other data,such as aggregated data from one or more other sources. Processing thedata may include comparing the data to one or more machine learningdatasets to predict or identify an output. In step 420, an output may begenerated, transmitted to and displayed, for example, via a display ofremote user mobile computing device 170. In some examples, the outputmay include an insurance product recommendation, a premium for aninsurance product, a discount or other incentive, or the like.

FIG. 5 illustrates one example process for aggregating data fromdisparate sources to generate an output according to one or more aspectsdescribed herein. The steps described with respect to FIG. 5 may beperformed by one or more of the various devices described herein, suchas the interactive test generation and control computing platform 110,the interactive test generation and control server 210, remote usermobile computing device, and the like. In some examples, one or more ofthe processes or steps described may be performed in real-time or nearreal-time.

In step 500, a request for a product may be received. In some examples,the request may be received from a user computing device, such as remoteuser mobile computing device 170. In step 502, user information may bereceived from, for instance, the remote user mobile computing device170. In some examples, the user information may include informationrequested by, for instance, the interactive test generation and controlcomputing platform 110 and may include information such as age, gender,location, and the like.

In step 504, one or more products may be identified. For instance, thereceived user information may be used to identify one or more productsfor which the user may be eligible and that meet the request for theproduct. In step 506, data may be received from a plurality of sources.For instance, data may be received from sources internal to an entityand/or sources external to an entity. For example, data may be receivedfrom one or more internal sources and may include data associated with auser, such as age, gender, location, whether the user is a homeowner,marital status, insurance history, claim history, driving behaviors, andthe like.

In some examples, data may be received from one or more external sourcesand may include data associated with the user, such asmedical/prescription history, consumer data such as transaction orpurchase history, behavioral information (e.g., gym membership, gymusage, and the like), as well as other external data. In some examples,at least some data may be received with permission of the user.

In some examples, data received may be data associated with a computingdevice associated with the user. For instance, the interactive testgeneration and control computing platform 110 may receive dataassociated with movement of a user's mobile computing device, how oftenthe device is in motion, type or motion or speed (e.g., walking vs.driving), types of applications often executed on the mobile device, andthe like.

In step 508, the received data may be aggregated and, in step 510, thedata may be processed to determine whether a user is eligible for theone or more products identified. In some examples, processing the datamay include using one or more machine learning datasets to determineeligibility, generate an output, and the like. In step 512, an outputmay be generated and or displayed, for instance, on a user computingdevice.

FIG. 6 illustrates one example process for renewing a product usinginteractive condition evaluation tests according to one or more aspectsdescribed herein. The steps described with respect to FIG. 6 may beperformed by one or more of the various devices described herein, suchas the interactive test generation and control computing platform 110,the interactive test generation and control server 210, remote usermobile computing device, and the like. In some examples, one or more ofthe processes or steps described may be performed in real-time or nearreal-time.

In step 600, a binding acceptance of an offered product or generatedoutput may be received. In some examples, upon generating and displayingan output to a user, the user may have an option to select to accept anoffer associated with the output. In some arrangements, accepting theoffer may be a binding agreement and, for instance, may be performedwithout conventional underwriting processes. In step 602, based on thebinding acceptance, the product or generated output may be enabled orenacted. For instance, if the generated output is an insurance policy,acceptance of the binding offer may cause the policy to go into effect.

In step 604, a determination may be made as to whether a predeterminedtime period has elapsed. For example, the selected product or output maybe enacted for a predetermined time period or term. Upon expiration ofthat term, the product may be cancelled if it is not renewed.Accordingly, in advance of the product being cancelled, and after apredetermined time (e.g., a predetermined time less than the term of theproduct) system may offer the user an option to renew. Accordingly, thesystem may determine whether the predetermined time period less than theterm of the product has elapsed. If not, the product may remain enabledor enacted in step 606.

If, in step 604, the time period has elapsed, the user may renew theproduct. In step 608, the user may be authenticated to the system. Forinstance, a notification may be transmitted to the user requesting theuser to login to the system for renewal. In some examples, logging infor renewal may include determining whether user authenticatingcredentials match pre-stored user authenticating credentials. In someexamples, credentials may include username and password, biometric datasuch as fingerprint, iris scan, facial recognition, and the like.

In step 610, one or more interactive condition evaluation tests may beidentified to determine whether the user is eligible to renew,parameters of the renewal, and the like. Similar to other aspectsdescribed herein, the interactive condition evaluation tests may beidentified based on user information, current product, and the like.

In step 612, the additional tests may be executed. Similar to otherarrangements described herein, the additional tests may be executed viaa computing device of the user (e.g., remote user mobile computingdevice 170).

In step 614, data may be collected from test execution and may beprocessed, for instance, using one or more machine learning datasets. Instep 616, based on the processed data, an output may be generated anddisplayed to the user. In some examples, the output may include an offeror recommendation to maintain or renew the product currently enabled orto modify the product (e.g., obtain a different product, modify one ormore parameters of the product, and the like).

FIG. 7 illustrates one example user interface that may be generated andtransmitted to a mobile device of a user. The user interface 700 mayinclude identification of a first test, instructions for performing thefirst test, and the like. The user may initiate the test by selecting“GO” or other option.

FIG. 8 illustrates one example user interface providing a generatedoutput. The interface 800 may include an indication of the product forwhich the user is eligible or product being offered, as well as a costassociated with the product. In some examples, a link may be provided toadditional information, parameters, term, conditions, and the like. Theinterface 800 may further include an option to accept the offer.Acceptance of the offer may bind the user in real-time, in at least someexamples.

FIG. 9 illustrates a block diagram of a computing device (or system) 901in a computer system 900 that may be used according to one or moreillustrative embodiments of the disclosure. The computing device 901 mayhave a processor 903 for controlling overall operation of the computingdevice 901 and its associated components, including RAM 905, ROM 907,input/output module 909, and memory 915. The computing device 901, alongwith one or more additional devices (e.g., terminals 950 and 951,security and integration hardware 960) may correspond to any of multiplesystems or devices, such as a user personal mobile computing device,computing platform, or a computer server, configured as described hereinfor collecting data, identifying and executing one or more interactivecondition evaluation tests, evaluating data, generating outputs, and thelike.

Input/Output (I/O) 909 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 901 mayprovide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual and/or graphical output. Software may be stored withinmemory 915 and/or storage to provide instructions to processor 903 forenabling computing device 901 to perform various actions. For example,memory 915 may store software used by the computing device 901, such asan operating system 917, application programs 919, and an associatedinternal database 921. The various hardware memory units in memory 915may include volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. Certain devices/systems within interactive test generationand control computing system may have minimum hardware requirements inorder to support sufficient storage capacity, analysis capacity, networkcommunication, etc. For instance, in some embodiments, one or morenonvolatile hardware memory units having a minimum size (e.g., at least1 gigabyte (GB), 2 GB, 5 GB, etc.), and/or one or more volatile hardwarememory units having a minimum size (e.g., 256 megabytes (MB), 512 MB, 1GB, etc.) may be used in a device 901 (e.g., a mobile computing device901, interactive test generation and control server 901, external server901, etc.), in order to store and execute interactive test generationand control software application, execute tests, collect and analyzedata, generate outputs, generate recommendations and/or incentives, etc.Memory 915 also may include one or more physical persistent memorydevices and/or one or more non-persistent memory devices. Memory 915 mayinclude, but is not limited to, random access memory (RAM) 905, readonly memory (ROM) 907, electronically erasable programmable read onlymemory (EEPROM), flash memory or other memory technology, CD-ROM,digital versatile disks (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store thedesired information and that can be accessed by processor 903.

Processor 903 may include a single central processing unit (CPU), whichmay be a single-core or multi-core processor (e.g., dual-core,quad-core, etc.), or may include multiple CPUs. Processor(s) 903 mayhave various bit sizes (e.g., 16-bit, 32-bit, 64-bit, 96-bit, 128-bit,etc.) and various processor speeds (ranging from 100 MHz to 5 Ghz orfaster). Processor(s) 903 and its associated components may allow thesystem 901 to execute a series of computer-readable instructions, forexample, to identify interactive condition evaluation tests, executingtests, collecting and analyzing data, generating outputs, and the like.

The computing device (e.g., a mobile computing device, computingplatform, server, external server, etc.) may operate in a networkedenvironment 900 supporting connections to one or more remote computers,such as terminals 950 and 951. The terminals 950 and 951 may be personalcomputers, servers (e.g., web servers, database servers), or mobilecommunication devices (e.g., mobile phones, portable computing devices,on-board vehicle-based computing systems, and the like), and may includesome or all of the elements described above with respect to thecomputing device 901. The network connections depicted in FIG. 9 includea local area network (LAN) 925 and a wide area network (WAN) 929, and awireless telecommunications network 933, but may also include othernetworks. When used in a LAN networking environment, the computingdevice 901 may be connected to the LAN 925 through a network interfaceor adapter 923. When used in a WAN networking environment, the device901 may include a modem 927 or other means for establishingcommunications over the WAN 929, such as network 931 (e.g., theInternet). When used in a wireless telecommunications network 933, thedevice 901 may include one or more transceivers, digital signalprocessors, and additional circuitry and software for communicating withwireless computing devices 940 (e.g., mobile phones, portable computingdevices, on-board vehicle-based computing systems, etc.) via one or morenetwork devices 935 (e.g., base transceiver stations) in the wirelessnetwork 933.

Also illustrated in FIG. 9 is a security and integration layer 960,through which communications may be sent and managed between the device901 (e.g., a user's personal mobile device, an interactive testgeneration and control computing platform or server, etc.) and theremote devices (950 and 951) and remote networks (925, 929, and 933).The security and integration layer 960 may comprise one or more separatecomputing devices, such as web servers, authentication servers, and/orvarious networking components (e.g., firewalls, routers, gateways, loadbalancers, etc.), having some or all of the elements described abovewith respect to the computing device 901. As an example, a security andintegration layer 960 of a mobile computing device, computing platform,or a server operated by an insurance provider, financial institution,governmental entity, or other organization, may comprise a set of webapplication servers configured to use secure protocols and to insulatethe server 901 from external devices 950 and 951. In some cases, thesecurity and integration layer 960 may correspond to a set of dedicatedhardware and/or software operating at the same physical location andunder the control of same entities as driving data analysis server 901.For example, layer 960 may correspond to one or more dedicated webservers and network hardware in an organizational datacenter or in acloud infrastructure supporting a cloud-based driving data analysissystem. In other examples, the security and integration layer 960 maycorrespond to separate hardware and software components which may beoperated at a separate physical location and/or by a separate entity.

As discussed below, the data transferred to and from various devices inthe computing system 900 may include secure and sensitive data, such asdevice usage data, application usage data, medical or personalinformation, test result data, and the like. Therefore, it may bedesirable to protect transmissions of such data by using secure networkprotocols and encryption, and also to protect the integrity of the datawhen stored on in a database or other storage in a mobile device,interactive test generation and control computing platform or server andother computing devices in the system 900, by using the security andintegration layer 960 to authenticate users and restrict access tounknown or unauthorized users. In various implementations, security andintegration layer 960 may provide, for example, a file-based integrationscheme or a service-based integration scheme for transmitting databetween the various devices in a system 900. Data may be transmittedthrough the security and integration layer 960, using various networkcommunication protocols. Secure data transmission protocols and/orencryption may be used in file transfers to protect to integrity of thedriving data, for example, File Transfer Protocol (FTP), Secure FileTransfer Protocol (SFTP), and/or Pretty Good Privacy (PGP) encryption.In other examples, one or more web services may be implemented withinthe various devices 901 in the system 900 and/or the security andintegration layer 960. The web services may be accessed by authorizedexternal devices and users to support input, extraction, andmanipulation of the data (e.g., device usage data, location data,vehicle data, etc.) between the various devices 901 in the system 900.Web services built to support system 900 may be cross-domain and/orcross-platform, and may be built for enterprise use. Such web servicesmay be developed in accordance with various web service standards, suchas the Web Service Interoperability (WS-I) guidelines. In some examples,a movement data and/or driving data web service may be implemented inthe security and integration layer 960 using the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between servers 901 and various clients 950 and 951 (e.g.,mobile devices, data analysis servers, etc.). SSL or TLS may use HTTP orHTTPS to provide authentication and confidentiality. In other examples,such web services may be implemented using the WS-Security standard,which provides for secure SOAP messages using XML encryption. In stillother examples, the security and integration layer 960 may includespecialized hardware for providing secure web services. For example,secure network appliances in the security and integration layer 960 mayinclude built-in features such as hardware-accelerated SSL and HTTPS,WS-Security, and firewalls. Such specialized hardware may be installedand configured in the security and integration layer 960 in front of theweb servers, so that any external devices may communicate directly withthe specialized hardware.

Although not shown in FIG. 9, various elements within memory 915 orother components in system 900, may include one or more caches, forexample, CPU caches used by the processing unit 903, page caches used bythe operating system 917, disk caches of a hard drive, and/or databasecaches used to cache content from database 921. For embodimentsincluding a CPU cache, the CPU cache may be used by one or moreprocessors in the processing unit 903 to reduce memory latency andaccess time. In such examples, a processor 903 may retrieve data from orwrite data to the CPU cache rather than reading/writing to memory 915,which may improve the speed of these operations. In some examples, adatabase cache may be created in which certain data from a database 921(e.g., interactive condition evaluation test result database, internaldata database, external data database, etc.) is cached in a separatesmaller database on an application server separate from the databaseserver. For instance, in a multi-tiered application, a database cache onan application server can reduce data retrieval and data manipulationtime by not needing to communicate over a network with a back-enddatabase server. These types of caches and others may be included invarious embodiments, and may provide potential advantages in certainimplementations of performing functions describes herein.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computer devices and systemcomponents described herein may be configured to communicate using anyof these network protocols or technologies.

Additionally, one or more application programs 919 may be used by thevarious computing devices 901 within an interactive test generation andcontrol computing system 900 (e.g., software applications, etc.),including computer executable instructions for identifying one or moreproducts, identifying one or more interactive condition evaluationtests, executing interactive condition evaluation tests, collectingdata, analyzing data, and the like, as described herein.

As discussed herein, various examples for generating an output based ondifferent types of data from different sources are described. In someexamples, machine learning may be used to generate one or more outputs.Using data from various different sources, as well as different types ofdata, may provide more accurate predictions of risk, mortality, and thelike, in order to generate and offer outputs that are more closelytailored to a user's needs.

Further, using the various types of data, as well as machine learning,may allow and entity generating an output to better align pricing with adetermined risk. In conventional systems, it may take several years toevaluate outputs, such as a determined risk for a particular user, apredicted mortality, or the like. By processing great volumes of data togenerate machine learning datasets, validation of risk predictions orassumptions, and the like may be performed much more quickly whichultimately may allow for pricing of products (e.g., insurance policies,and the like) at a more granular level.

As discussed, one or more interactive condition evaluation tests may beused to collect data associated with a user, assess conditions of theuser, determine whether a user is eligible for a product, and/orgenerate an output (e.g., an insurance policy to offer, a premium for aninsurance policy, a discount or incentive, or the like). Below areseveral example interactive condition evaluation tests that may be usedwith one or more arrangements described herein. Various other tests maybe used without departing from the invention and nothing in the examplesbelow should be viewed as limiting the interactive condition evaluationtests to only these examples.

In one example, an interactive condition evaluation test may includeevaluating mobility of a user. Accordingly, the interactive testgeneration and control computing platform 110 may generate a userinterface including instructions for executing a mobility test using amobile device of the user. The user may receive the interface which maybe displayed on the mobile device. In some examples, the test mayinclude instructing a user to walk, run, jog, or the like, apredetermined distance. Sensors within the mobile device may track thedistance walked, time for walking the distance, pace of the user, andthe like. In some examples, data related to heart rate of the user,pulse of the user, and the like, may also be collected by one or moresensors in the mobile device. This information may then be transmittedto the interactive test generation and control computing platform 110for processing and analysis.

In another example, a user may be instructed to walk, run, jog, or thelike, on a treadmill for a predetermined time, at a predetermined pace,or the like, while carrying the user's mobile device. Sensors within thedevice may detect and/or collect data associated with performance of thetest, heart rate, pulse, and the like, and this information may betransmitted to the interactive test generation and control computingplatform 110 for processing and analysis.

In some arrangements, for either of the above-described exampleinteractive tests, video may be captured of the user while performingthe test. This video may be further evaluated to determine a gait ofuser, how easily the user managed the interactive test, or the like.

In other example interactive tests, a user may be instructed to performone or more other physical functions (e.g., outside of walking, runningor the like). For instance, a user may be requested to hold his or herarms in front of his or her body for as long as possible while holdingthe mobile device. One or more sensors within the mobile device maycollect data associated with a position of the mobile device, time in aparticular position, and the like, and this information may betransmitted to the interactive test generation and control computingplatform 110 for processing and analysis.

In some examples, similar physical tests may be performed with a user'slegs (e.g., sit in chair and extend legs).

In some examples, one or more interactive tests may test a reflex of auser. For instance, an image may be displayed on a mobile device of auser with instructions to touch one or more icons indicating a certainitem (e.g., a plurality of icons are displayed, touch or select all thatare a particular object). The sensors and/or other mobile devicecomponents may detect not only how many correct answers the userprovided but also how quickly the user was able to respond (e.g., howquickly the user could touch the screen). This data may then betransmitted to the interactive test generation and control computingplatform 110 for processing an analysis.

In another example interactive condition evaluation test for reflexes,the user may be instructed to touch a display of the mobile device asquickly as possible upon seeing a particular prompt. The mobile devicemay then collect data associated with how quickly the user touched thedisplay and may transmit that data for processing and analysis.

Additional interactive condition evaluation tests may be directed toevaluating a user's recall. For instance, a user may be provided with alist of words that they may view for a predetermined time period. Afterthe time period expires, the user may be requested to input as manywords as he or she can remember. The words may be input via a keyboard(e.g., virtual or physical) or spoken.

In some examples, one or more interactive condition evaluation tests maybe used to evaluate a lung capacity or respiration of a user. Forinstance, a tobacco user may have a reduced lung capacity, increasedrespiration rate, or the like. Accordingly, one or more interactivecondition evaluation tests may include having a user exhale onto amobile device and one or more sensors may be detect a number ofexhalations, a velocity of the breath, a rate of exhalations, and thelike. In some examples, the user may exhale onto a microphone of themobile device and the audio received may be processed to determine astrength of exhale, number of exhalations, and the like. In someexamples, one or more test may request a user to exhale for apredetermined amount of time while positioned a predetermined distancefrom the mobile device. This information may be transmitted to theinteractive test generation and control computing platform 110 forprocessing and analysis.

In some examples, one or more interactive condition evaluation tests mayinclude monitoring sleep habits of a user. This data may then betransmitted for processing and analysis.

In some examples, one or more interactive condition evaluation tests mayincluding requesting a user to capture one or more images of particularbody parts, or the like. For instance, images of the user may be used todetermine height, weight, overall health appearance, and the like. Insome examples, the user may be requested to submit particular images.For instance, a close up image of an eye of a user may be used todetermine one or more health issues, such as coronary disease,hypertension, diabetes, and the like.

In some examples, the system may generate a plurality of tests forexecution. A user may, in some examples, complete some or all of thetests. If the user completes fewer than all of the tests, the outputgenerated may be impacted by completion of fewer than all of theidentified tests (e.g., output may include a higher premium for a policythan a user completing all tests, discount or incentive may be differentfrom a user who completed all tests, or the like).

Although various aspects described herein are described as beingexecuted by a mobile device of a user, a mobile device may, in someexamples, include a wearable device, such as a fitness tracker. One ormore tests may be executed via the fitness tracker, data may becollected and transmitted, and the like. In some examples, data from afitness tracker or other wearable device may be used in combination withother data (e.g., may be used as data from an external source,collected, aggregated and processed, as discussed herein).

Data from sources other than the interactive condition evaluation testsmay also be used, as discussed herein. For instance, data from internalsources and/or external sources may be used to evaluate risk, generateoutputs, provide offers, and the like.

For instance, in some examples, data associated with usage of a mobiledevice may be collected and used in analyzing eligibility, generatingoutputs, and the like. For instance, types of applications accessed by auser, how often applications are accessed, and the like, may becollected and used in the analysis. For example, if a user executes oneor more health or fitness applications on a mobile device, that mayindicate a healthy lifestyle. Alternatively, if the mobile device isoften used for streaming video, that may indicated a more sedentarylifestyle. These factors may be used to evaluate eligibility, determinean output, or the like.

As discussed herein, various types of internal data may be collected andused in making various output determinations. For instance, if theentity implementing the system is an insurance provider, data associatedwith home insurance, auto insurance, life insurance, and the like may beused. In some examples, historical data such as claims data, and thelike, may be used in generating one or more machine learning datasets.Data associated with a particular user requesting a product may also beextracted and used to generate an output. For example, user claimhistory, vehicle operational data or driving behaviors (e.g., ascollected from a vehicle of the user, mobile device of the user, or thelike), may be used.

As also discussed herein, various types of external data may becollected and used in making various output determinations. In someexamples, the external data may be received from one or more sourcesexternal to an entity implementing the system. The external sources mayinclude publicly available information, anonymous information,information collected with permission of the user, and the like. Someexamples of external data are provided below. However, various othertypes of external data may be collected and used without departing fromthe invention and the examples below should not be viewed as limitingexternal data to only these types of data.

In some examples, consumer data such as transaction data and the likemay be used. For instance, data collected via a loyalty program atgrocery stores, department stores, and the like, may be used to evaluatea lifestyle of user. Data such as types of purchases made, locations ofpurchase, frequency of purchase, amount of purchase, and the like may beconsidered. In some examples, purchases made at a grocery store (e.g.,healthy foods, cigarettes, alcohol, or the like) may be collected andevaluated to generate one or more outputs.

In some examples, external data such as medical information of the usermay be collected and used in the analysis. This data may be collectedwith permission of the user and may include prescriptions used, medicaldiagnosis, recent lab results, recent results of a physical examination,family medical history, and the like.

In some arrangements, other behavioral data may be used. For instance,whether a user has a membership to a gym, how often the user visits thegym, and the like, may be used. In some examples, global positioningsystem data may be used to determine or verify a position of a user(e.g., user visits a gym 5 days/week). Additionally or alternatively,detecting behaviors such as marathon running, 5K running, or the like,may be detected from sensor data, as well as time, pace, and the like.This data may be collected and used in evaluation for generatingoutputs.

Data associated with occupation and/or hobbies may also be considered.For instance, detection of, for instance, skydiving, as a hobby (e.g.,based on altimeter sensor data from a mobile device) may indicate a riskfactor for a user. In some examples, data associated with an occupationmay be collected. For instance, detection of frequent changes inaltitude, speed, and the like, may indicate a user is a flightattendant, pilot, or the like. This information may be used inevaluation.

In some examples, user data may be collected over a period of time todetermine how sedentary a life a user lives. For instance, the movementof the mobile device may be tracked via one or more sensors and thatinformation may be transmitted for processing and analysis. In someexamples, this data may be collected during an eligibility evaluationprocess (e.g., before an output is generated, an offer is provided, orthe like). Additionally or alternatively, the data may be collectedduring a term of, for instance, an insurance policy, to monitor a user'slifestyle. In some examples, historical data from a time prior to theuser requesting a product may be collected and evaluated to identifypotential risk. Data may also be collected after the user has purchasedthe product to continue to evaluate risk. This continuous or continuedcollection may be also be used for dynamic pricing (e.g., pricing thatmay change based on detected behaviors) and/or for renewal of a product.

As discussed herein, in some examples, a user may accept a generatedoutput or offer and a binding agreement may be made. In somearrangements, one or more of the data collection, processing, offer andacceptance may be performed in real-time. In some examples, the bindingagreement may be based solely on the data collected from interactivecondition evaluation tests, internal data, external data, and the like(e.g., without traditional underwriting, physical examination or thelike). In other examples, a user may be provided with an output having afirst price. Acceptance of the offer may include the user agreeing tothe first price, however, an incentive may be generated for a user toprovide additional information, such as recent medical examinationresults, lab work, or the like. Accordingly, a rebate, refund, credit,or the like, may be offered for providing this additional information.

In some examples, a user may also permit an entity to use the collecteddata, generated outputs, test results, and the like in determiningeligibility for one or more other products. For instance, a system maygenerate a recommended other product (e.g., long term care insurance,auto insurance, or the like) and the data collected may be used toevaluation risk, eligibility, and the like. In some examples, the datamay be used to evaluate requests made by the user for additionalproducts.

As discussed above, biometric data such as fingerprints and the like,and/or facial recognition data may be used to authenticate a user,provide additional functionality, and the like. For instance, uponinitiating an interactive condition evaluation test, a user may berequested to capture an image of himself or herself. Facial recognitionmay then be used to confirm that the image captured corresponding to theuser. In some examples, public records may be used to confirm thisinformation. In other examples, the user may be asked to provide animage of, for instance, a driver's license. This may then be compared toa captured image to verify the identity of the user.

In some arrangements, fingerprint or other biometric data may also beused. For instance, a user may submit a fingerprint with acceptance ofan offer, for an insurance policy or the like. If a claim is then madeagainst the policy, or a modification is requested, the user mayauthenticate by submitting a fingerprint.

In another example, a beneficiary of an insurance policy may beidentified by his or her fingerprint. Accordingly, the beneficiary maysubmit the fingerprint upon a user purchasing the policy. Thebeneficiary may then submit a fingerprint to submit a claim.

In some arrangements, one or more aspects described herein may beembodied in an application executing on a computing device of a user. Insome arrangements, upon opening the application, various functionalitymay be enabled. For instance, sensors may be activated, permission maybe given to collect data, and the like. Although various aspectsdescribed herein are described with respect to life insurance policies,one or more aspects described herein may be used to evaluate eligibilityfor other products or services, such as auto insurance, homeownersinsurance, long term care insurance, and the like.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,Application-Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination.Furthermore, such aspects may take the form of a computer programproduct stored by one or more computer-readable storage media havingcomputer-readable program code, or instructions, embodied in or on thestorage media. In addition, various signals representing data or eventsas described herein may be transferred between a source and adestination in the form of light or electromagnetic waves travelingthrough signal-conducting media such as metal wires, optical fibers, orwireless transmission media (e.g., air or space). In general, the one ormore computer-readable media may be and/or include one or morenon-transitory computer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,one or more steps described with respect to one figure may be used incombination with one or more steps described with respect to anotherfigure, and/or one or more depicted steps may be optional in accordancewith aspects of the disclosure.

The invention claimed is:
 1. An interactive test generation and controlcomputing platform, comprising: a processing unit comprising aprocessor; and a memory unit storing computer-executable instructions,which when executed by the processing unit, cause the interactive testgeneration and control computing platform to: identify a firstinteractive condition evaluation test to be executed on a user computingdevice; transmit a signal to the user computing device enablingfunctionality of one or more sensors in the user computing device andassociated with the first interactive condition evaluation test;generate a first user interface providing instructions for performingthe first interactive condition evaluation test; transmit the generatedfirst user interface to the user computing device; initiate the firstinteractive condition evaluation test on the user computing device;after initiating the first interactive condition evaluation test,collect data from the enabled one or more sensors; process, based on oneor more machine learning datasets, the collected data to determine anoutput for the user; and transmit the output to the user computingdevice.
 2. The interactive test generation and control computingplatform of claim 1, further including instructions that, when executed,cause the interactive test generation and control computing platform to:determine whether a second interactive condition evaluation test hasbeen identified for execution; and responsive to determining that thesecond interactive condition evaluation test has been identified forexecution, initiating the second interactive condition evaluation teston the user computing device.
 3. The interactive test generation andcontrol computing platform of claim 1, further including instructionsthat, when executed, cause the interactive test generation and controlcomputing platform to: receive user input requesting a product orservice, and identify one or more products for evaluation in response toreceiving user input requesting the product or service.
 4. Theinteractive test generation and control computing platform of claim 1,wherein the processing, based on one or more machine learning datasets,the collected data to determine an output for the user further includesdetermining eligibility of the user for one or more products.
 5. Theinteractive test generation and control computing platform of claim 4,further including instructions that, when executed, cause theinteractive test generation and control computing platform to: receivedata from an internal computing device; receive data from an externalcomputing device; aggregate the data received from the internalcomputing device and the external computing device; and process, basedon the one or more machine learning datasets, the received data from theinternal computing device and the received data from the externalcomputing device to determine the eligibility of the user for the one ormore products.
 6. The interactive test generation and control computingplatform of claim 5, wherein the data from the external computing deviceincludes data associated with at least one of: health information of theuser and behavior information of the user.
 7. The interactive testgeneration and control computing platform of claim 1, wherein the firstinteractive condition evaluation test includes an instruction to walkfor a predetermined distance.
 8. The interactive test generation andcontrol computing platform of claim 1, wherein the first interactivecondition evaluation test includes instructions to respond to aplurality of cognitive skills questions via the user computing device.9. A method, comprising: at a computing platform comprising at least oneprocessor, memory, and a communication interface: identifying, by the atleast one processor, a first interactive condition evaluation test to beexecuted on a user computing device; transmitting, by the at least oneprocessor, a signal to the user computing device enabling functionalityof one or more sensors in the user computing device and associated withthe first interactive condition evaluation test; generating, by the atleast one processor, a first user interface providing instructions forperforming the first interactive condition evaluation test;transmitting, by the at least one processor, the generated first userinterface to the user computing device; initiating the first interactivecondition evaluation test on the user computing device; after initiatingthe first interactive condition evaluation test, collecting data fromthe enabled one or more sensors; processing, by the at least oneprocessor and based on one or more machine learning datasets, thecollected data to determine an output for the user; and transmitting theoutput to the user computing device.
 10. The method of claim 9, furtherincluding: determining, by the at least one processor, whether a secondinteractive condition evaluation test has been identified for execution;and responsive to determining that the second interactive conditionevaluation test has been identified for execution, initiating, by the atleast one processor, the second interactive condition evaluation test onthe user computing device.
 11. The method of claim 9, further including:receiving user input requesting a product or service, and identifyingone or more products for evaluation in response to receiving user inputrequesting the product or service.
 12. The method of claim 9, whereinthe processing, based on one or more machine learning datasets, thecollected data to determine an output for the user further includesdetermining eligibility of the user for one or more products.
 13. Themethod of claim 9, wherein the first interactive condition evaluationtest includes an instruction to walk for a predetermined distance. 14.The method of claim 9, wherein the first interactive conditionevaluation test includes instructions to respond to a plurality ofcognitive skills questions via the user computing device.
 15. One ormore non-transitory computer-readable media storing instructions that,when executed by a computing platform comprising at least one processor,memory, and a communication interface, cause the computing platform to:identify a first interactive condition evaluation test to be executed ona user computing device; transmit a signal to the user computing deviceenabling functionality of one or more sensors in the user computingdevice and associated with the first interactive condition evaluationtest; generate a first user interface providing instructions forperforming the first interactive condition evaluation test; transmit thegenerated first user interface to the user computing device; initiatethe first interactive condition evaluation test on the user computingdevice; after initiating the first interactive condition evaluationtest, collect data from the enabled one or more sensors; process, basedon one or more machine learning datasets, the collected data todetermine an output for the user; and transmit the output to the usercomputing device.
 16. The one or more non-transitory computer-readablemedia of claim 15, further including instructions that, when executed,cause the computing platform to: determine whether a second interactivecondition evaluation test has been identified for execution; andresponsive to determining that the second interactive conditionevaluation test has been identified for execution, initiating the secondinteractive condition evaluation test on the user computing device. 17.The one or more non-transitory computer-readable media of claim 15,further including instructions that, when executed, cause the computingplatform to: receive user input requesting a product or service, andidentify one or more products for evaluation in response to receivinguser input requesting the product or service.
 18. The one or morenon-transitory computer-readable media of claim 15, wherein theprocessing, based on one or more machine learning datasets, thecollected data to determine an output for the user further includesdetermining eligibility of the user for one or more products.
 19. Theone or more non-transitory computer-readable media of claim 15, whereinthe first interactive condition evaluation test includes an instructionto walk for a predetermined distance.
 20. The one or more non-transitorycomputer-readable media of claim 15, wherein the first interactivecondition evaluation test includes instructions to respond to aplurality of cognitive skills questions via the user computing device.